Imputation of a Variable with Grouped Values
Imputes a variable with continuous values whose original values are only available as grouped values.
mice.impute.grouped(y, ry, x, low=NULL, upp=NULL, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
low |
Vector with lower bound of grouping interval |
upp |
Vector with upper bound of grouping interval |
... |
Further arguments to be passed |
A vector of length nmis=sum(!ry) with imputed values.
This function uses the grouped::grouped
function in the grouped package.
## Not run:
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# EXAMPLE 1: Imputation of grouped data
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data(data.ma06)
data <- data.ma06
# define the variable "FC_imp" which should contain the variables to be imputed
data$FC_imp <- NA
V <- ncol(data)
# variables not to be used for imputation
vars_elim <- c("id", "FC","FC_low","FC_upp")
# define imputation methods
impM <- rep("norm", V)
names(impM) <- colnames(data)
impM[ vars_elim ] <- ""
impM[ "FC_imp" ] <- "grouped"
# define predictor matrix
predM <- 1 - diag( 0, V)
rownames(predM) <- colnames(predM) <- colnames(data)
predM[vars_elim, ] <- 0
predM[,vars_elim] <- 0
# define lower and upper boundaries of the grouping intervals
low <- list("FC_imp"=data$FC_low )
upp <- list("FC_imp"=data$FC_upp )
# perform imputation
imp <- mice::mice( data, method=impM, predictorMatrix=predM,
m=1, maxit=3, allow.na=TRUE, low=low, upp=upp)
head( mice::complete(imp))
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